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AI Engineer with 5 years of engineering and 3 years building production AI/ML systems. I architect agentic RAG systems, predictive ML pipelines, and compliance automation that run in enterprise environments.
class AIEngineer:
def __init__(self):
self.focus = [
"Agentic RAG & Multi-Agent Orchestration",
"Predictive Maintenance & Anomaly Detection",
"MLOps & Production ML Systems"
]
def deploy(self, model) -> Production:
return model.notebook_to_production()|
Production RAG system with vector search and traceable citations. Retrieves precise answers from uploaded PDFs with [1] [2] source references pointing to exact documents and pages. Next.js 16 • React 19 • TypeScript • Supabase pgvector • Voyage AI • Groq • Vercel
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Production MLOps platform processing 50k+ sensor readings through Bronze/Silver/Gold medallion architecture. PySpark ETL pipelines feed real-time fleet health dashboards with automated drift detection and retraining triggers. PySpark • Delta Lake • PostgreSQL • Streamlit • Plotly • Docker • Terraform
|
experiments/
├── predictive-agent/ # LSTM time-series model for remaining useful life
├── compliance-agent/ # Multi-agent RAG for regulatory compliance
├── anomaly-agent/ # Streaming anomaly detection with root cause analysis
└── vision-agent/ # VLM for structured scene understanding (Qwen2-VL)
| Experiment | Stack | Code | Demo |
|---|---|---|---|
| Predictive Agent | LSTM · Scikit-Learn · Plotly · Docker |  |
 |
| Compliance Agent | PydanticAI · DSPy · Mistral · FastAPI | |
|
| Anomaly Agent | Isolation Forest · Gradio · Time-Series | |
|
| Vision Agent | Qwen2-VL · Transformers · Gradio | |
|
+ LangGraph → Refactored FunctionMessage patterns, Enhanced fine-tuning docs
+ Pydantic → Core library contributions
+ AutoGen → Fixed Azure AI Client streaming stability
+ CrewAI → URL validation for Azure Gateways
+ Transformers → Documentation improvements
┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ AI/ML │
│ ├── Core: PyTorch, Scikit-Learn, LSTM, Isolation Forest, Time-Series │
│ ├── Agents: LangGraph, AutoGen, CrewAI, PydanticAI │
│ ├── RAG: pgvector, ChromaDB, Voyage AI, LlamaIndex │
│ └── MLOps: Model Monitoring, Drift Detection, A/B Testing, CI/CD │
├────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ Infrastructure │
│ ├── Cloud: Azure ML, GCP Vertex AI, AWS SageMaker │
│ ├── Containers: Docker, Kubernetes (AKS/GKE) │
│ └── IaC: Terraform, GitHub Actions │
├────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ Data & Pipelines │
│ ├── Processing: Python, SQL, PySpark, PostgreSQL │
│ ├── Serving: FastAPI, REST APIs, Streaming Pipelines │
│ └── Domain: SCADA/Sensor Data, Feature Engineering │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘
|
University of Colorado Boulder — Expected 2027 |
5 years engineering · 3 years production AI/ML systems |
From adaptive learning engines to real-time blockchain fraud detection to industrial predictive maintenance. I take models from notebooks to production.